Neutrosophic Reducible Weighted Maclaurin Symmetric Mean for Undergraduate Teaching Audit and Evaluation

Abstract

The undergraduate teaching audit and evaluation (UTAE) is critically important for the university to promote the establishment of a quality assurance system and improve the quality of teaching. In considering the case of UTAE, the essential question arises strong ambiguity and interaction. The Maclaurin symmetric mean (MSM), as a significant information integration tool, can seize the interrelation among multiple input values more effectively. A series of weighted MSMs have been developed to cope with diverse neutrosophic information aggregation issues by reason that the attribute variables are frequently desparate. Nevertheless, these weighted form of MSM operators is not idempotent. Moreover, the weight MSM cannot degrade into the MSM when their weights information is equivalent. In other words, it signifies without the reducibility. To resolve two issues, we develop the single-valued neutrosophic reducible weighted MSM (SVNRWMSM) operator and the single-valued neutrosophic reducible weighted dual MSM (SVNRWDMSM) operator. Meanwhile, certain interesting properties and some special cases of the SVNRWMSM and SVNRWDMSM operators are explored in detail. Afterward, we develop two multiple attribute decision-making methods based on SVNRWMSM and SVNRWDMSM. The validity of algorithms is illustrated by an undergraduate teaching evaluation issue, along with the sensitivity analysis of diverse parameter values on the ranking. Finally, a comparison of the developed with the existing single-valued neutrosophic decision-making algorithms has been executed for displaying their efficiency.